DV_SProject
How to Reproduce Our Work:
Step 1: Create GitHub Repo with necessary folders for partners to collaborate 00 Doc contains the .Rmd and HTML files 01 Data contains the data set that was worked with 02 Shiny contains the server and user interface (ui) for the Shiny App Step 2: Use an interesting data set with data that may be easily and interestingly manipulated Step 3: Import the csv file into SQL Developer Step 4: Save your app’s server.R and ui.R script inside the 02 Shiny folder Step 5: Launch the shinyApp with runApp and exit by clicking escape
Data Summary:
Below is the dataset and first ten rows
require("jsonlite")## Loading required package: jsonlite
##
## Attaching package: 'jsonlite'
##
## The following object is masked from 'package:utils':
##
## Viewrequire("RCurl")## Loading required package: RCurl
## Loading required package: bitopsrequire(ggplot2)## Loading required package: ggplot2require(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondf <- data.frame(fromJSON(getURL(URLencode('skipper.cs.utexas.edu:5001/rest/native/?query="select * from edudata"'),httpheader=c(DB='jdbc:oracle:thin:@sayonara.microlab.cs.utexas.edu:1521:orcl', USER='C##cs329e_gmg954', PASS='orcl_gmg954', MODE='native_mode', MODEL='model', returnDimensions = 'False', returnFor = 'JSON'), verbose = TRUE)), ignoreNULL = FALSE)
head(df)## COUNTRY_NAME COUNTRY_CODE
## 1 Aruba ABW
## 2 Aruba ABW
## 3 Aruba ABW
## 4 Aruba ABW
## 5 Aruba ABW
## 6 Aruba ABW
## INDICATOR_NAME
## 1 Lower secondary school starting age (years)
## 2 Lower secondary completion rate, female (% of relevant age group)
## 3 Lower secondary completion rate, male (% of relevant age group)
## 4 Lower secondary completion rate, total (% of relevant age group)
## 5 Secondary education, duration (years)
## 6 Secondary education, pupils
## INDICATOR_CODE X2000 X2001 X2002
## 1 SE.SEC.AGES 12 12 12
## 2 SE.SEC.CMPT.LO.FE.ZS 96.4229965209961 100.603302001953 100.29239654541
## 3 SE.SEC.CMPT.LO.MA.ZS 93.1087036132812 95.9337005615234 94.1003036499023
## 4 SE.SEC.CMPT.LO.ZS 94.7530975341797 98.2667999267578 97.2099990844727
## 5 SE.SEC.DURS 5 5 5
## 6 SE.SEC.ENRL 6178 6428 6757
## X2003 X2004 X2005 X2006
## 1 12 12 12 12
## 2 94.1010971069336 92.4527969360352 87.5162963867188 null
## 3 84.8787002563477 82.5789031982422 87.6658020019531 null
## 4 89.5258026123047 87.5595016479492 87.5903015136719 null
## 5 5 5 5 5
## 6 6869 6973 7116 7439
## X2007 X2008 X2009 X2010 X2011 X2012 X2013 X2014
## 1 12 12 12 12 12 12 12 12
## 2 89.7855987548828 null 99.6067962646484 null null null null null
## 3 88.1463012695312 null 92.8752975463867 null null null null null
## 4 88.9660034179688 null 96.1911010742188 null null null null null
## 5 5 5 5 5 5 5 5 5
## 6 7853 7270 7439 7342 7378 8377 null null
## X2015 ignoreNULL
## 1 null FALSE
## 2 null FALSE
## 3 null FALSE
## 4 null FALSE
## 5 null FALSE
## 6 null FALSEData Explanation and Overal Manipulation:
Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. This data on education was compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country.
Below are all of the key indicators from our project:
require("jsonlite")
require("RCurl")
require(ggplot2)
require(dplyr)
df <- data.frame(fromJSON(getURL(URLencode('skipper.cs.utexas.edu:5001/rest/native/?query="select distinct(indicator_name) from edudata order by indicator_name desc"'),httpheader=c(DB='jdbc:oracle:thin:@sayonara.microlab.cs.utexas.edu:1521:orcl', USER='C##cs329e_gmg954', PASS='orcl_gmg954', MODE='native_mode', MODEL='model', returnDimensions = 'False', returnFor = 'JSON'), verbose = TRUE)), ignoreNULL = FALSE)
df## INDICATOR_NAME
## 1 Unemployment, total (% of total labor force) (modeled ILO estimate)
## 2 Unemployment, male (% of male labor force) (modeled ILO estimate)
## 3 Unemployment, female (% of female labor force) (modeled ILO estimate)
## 4 Trained teachers in primary education, male (% of male teachers)
## 5 Trained teachers in primary education, female (% of female teachers)
## 6 Trained teachers in primary education (% of total teachers)
## 7 Tertiary education, academic staff (% female)
## 8 Secondary education, vocational pupils (% female)
## 9 Secondary education, vocational pupils
## 10 Secondary education, teachers, female
## 11 Secondary education, teachers (% female)
## 12 Secondary education, teachers
## 13 Secondary education, pupils (% female)
## 14 Secondary education, pupils
## 15 Secondary education, general pupils (% female)
## 16 Secondary education, general pupils
## 17 Secondary education, duration (years)
## 18 School enrollment, tertiary, male (% gross)
## 19 School enrollment, tertiary, female (% gross)
## 20 School enrollment, tertiary (gross], gender parity index (GPI)
## 21 School enrollment, tertiary (% gross)
## 22 School enrollment, secondary, private (% of total secondary)
## 23 School enrollment, secondary, male (% net)
## 24 School enrollment, secondary, male (% gross)
## 25 School enrollment, secondary, female (% net)
## 26 School enrollment, secondary, female (% gross)
## 27 School enrollment, secondary (gross], gender parity index (GPI)
## 28 School enrollment, secondary (% net)
## 29 School enrollment, secondary (% gross)
## 30 School enrollment, primary, private (% of total primary)
## 31 School enrollment, primary, male (% net)
## 32 School enrollment, primary, male (% gross)
## 33 School enrollment, primary, female (% net)
## 34 School enrollment, primary, female (% gross)
## 35 School enrollment, primary and secondary (gross], gender parity index (GPI)
## 36 School enrollment, primary (gross], gender parity index (GPI)
## 37 School enrollment, primary (% net)
## 38 School enrollment, primary (% gross)
## 39 School enrollment, preprimary, male (% gross)
## 40 School enrollment, preprimary, female (% gross)
## 41 School enrollment, preprimary (% gross)
## 42 Repeaters, secondary, total (% of total enrollment)
## 43 Repeaters, secondary, male (% of male enrollment)
## 44 Repeaters, secondary, female (% of female enrollment)
## 45 Repeaters, primary, total (% of total enrollment)
## 46 Repeaters, primary, male (% of male enrollment)
## 47 Repeaters, primary, female (% of female enrollment)
## 48 Pupil-teacher ratio, secondary
## 49 Pupil-teacher ratio, primary
## 50 Progression to secondary school, male (%)
## 51 Progression to secondary school, female (%)
## 52 Progression to secondary school (%)
## 53 Primary school starting age (years)
## 54 Primary education, teachers (% female)
## 55 Primary education, teachers
## 56 Primary education, pupils (% female)
## 57 Primary education, pupils
## 58 Primary education, duration (years)
## 59 Primary completion rate, total (% of relevant age group)
## 60 Primary completion rate, male (% of relevant age group)
## 61 Primary completion rate, female (% of relevant age group)
## 62 Prevalence of HIV, total (% of population ages 15-49)
## 63 Population ages 15-64 (% of total)
## 64 Population ages 0-14 (% of total)
## 65 Persistence to last grade of primary, total (% of cohort)
## 66 Persistence to last grade of primary, male (% of cohort)
## 67 Persistence to last grade of primary, female (% of cohort)
## 68 Persistence to grade 5, total (% of cohort)
## 69 Persistence to grade 5, male (% of cohort)
## 70 Persistence to grade 5, female (% of cohort)
## 71 Net intake rate in grade 1, male (% of official school-age population)
## 72 Net intake rate in grade 1, female (% of official school-age population)
## 73 Net intake rate in grade 1 (% of official school-age population)
## 74 Mortality rate, under-5 (per 1,000 live births)
## 75 Lower secondary school starting age (years)
## 76 Lower secondary completion rate, total (% of relevant age group)
## 77 Lower secondary completion rate, male (% of relevant age group)
## 78 Lower secondary completion rate, female (% of relevant age group)
## 79 Literacy rate, youth total (% of people ages 15-24)
## 80 Literacy rate, youth male (% of males ages 15-24)
## 81 Literacy rate, youth female (% of females ages 15-24)
## 82 Literacy rate, adult total (% of people ages 15 and above)
## 83 Literacy rate, adult male (% of males ages 15 and above)
## 84 Literacy rate, adult female (% of females ages 15 and above)
## 85 Labor force, total
## 86 Labor force, female (% of total labor force)
## 87 Labor force with tertiary education, male (% of male labor force)
## 88 Labor force with tertiary education, female (% of female labor force)
## 89 Labor force with tertiary education (% of total)
## 90 Labor force with secondary education, male (% of male labor force)
## 91 Labor force with secondary education, female (% of female labor force)
## 92 Labor force with secondary education (% of total)
## 93 Labor force with primary education, male (% of male labor force)
## 94 Labor force with primary education, female (% of female labor force)
## 95 Labor force with primary education (% of total)
## 96 Gross intake ratio in first grade of primary education, total (% of relevant age group)
## 97 Gross intake ratio in first grade of primary education, male (% of relevant age group)
## 98 Gross intake ratio in first grade of primary education, female (% of relevant age group)
## 99 Government expenditure per student, tertiary (% of GDP per capita)
## 100 Government expenditure per student, secondary (% of GDP per capita)
## 101 Government expenditure per student, primary (% of GDP per capita)
## 102 Government expenditure on education, total (% of government expenditure)
## 103 Government expenditure on education, total (% of GDP)
## 104 Expenditure on tertiary education (% of government expenditure on education)
## 105 Expenditure on secondary education (% of government expenditure on education)
## 106 Expenditure on primary education (% of government expenditure on education)
## 107 Current education expenditure, total (% of total expenditure in public institutions)
## 108 Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)
## 109 Current education expenditure, secondary (% of total expenditure in secondary public institutions)
## 110 Current education expenditure, primary (% of total expenditure in primary public institutions)
## 111 Children out of school, primary, male
## 112 Children out of school, primary, female
## 113 Children out of school, primary
## 114 All education staff compensation, total (% of total expenditure in public institutions)
## 115 All education staff compensation, tertiary (% of total expenditure in tertiary public institutions)
## 116 All education staff compensation, secondary (% of total expenditure in secondary public institutions)
## 117 All education staff compensation, primary (% of total expenditure in primary public institutions)
## 118 Adjusted net enrollment rate, primary, male (% of primary school age children)
## 119 Adjusted net enrollment rate, primary, female (% of primary school age children)
## 120 Adjusted net enrollment rate, primary (% of primary school age children)
## ignoreNULL
## 1 FALSE
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## 120 FALSEShiny Methodology
- Shiny is an R package that makes it easy to build interactive web applications (apps) straight from R.
- Shiny ui.R scripts use the function fluidPage to create a display that automatically adjusts to the dimensions of your user’s browser window. You lay out your app by placing elements in the fluidPage function.The user-interface (ui) script controls the layout and appearance of your app. It is defined in a source script named ui.R. Additionally, the server.R script contains the instructions that your computer needs to build your app.
- Most of the script is wrapped in a call to renderPlot. This is an expression that generates a histogram. renderPlot indicates that the expression is “reactive” and should automatically re-execute whenver input changes. It also specifies that the output type is a plot.
Shiny App Walkthrough:
- link to our app:
- Dashboard App:
- Bar Chart
- Cross Tab + KPI
- Scatter Plot
Visualizations:
